Kottlors Jonathan, Fervers Philipp, Geißen Simon, Gertz Roman Johannes, Bremm Johannes, Rinneburger Miriam, Weisthoff Mathilda, Shahzad Rahil, Maintz David, Persigehl Thorsten
Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne (UOC), Cologne, Germany.
Division of Cardiology, Pneumology, Angiology and Intensive Care, University of Cologne (UOC), Cologne, Germany.
Quant Imaging Med Surg. 2023 Feb 1;13(2):1058-1070. doi: 10.21037/qims-22-718. Epub 2023 Jan 14.
Diagnosing a coronavirus disease 2019 (COVID-19) infection with high specificity in chest computed tomography (CT) imaging is considered possible due to distinctive imaging features of COVID-19 pneumonia. Since other viral non-COVID pneumonia show mostly a different distribution pattern, it is reasonable to assume that the patterns observed caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are a consequence of its genetically encoded molecular properties when interacting with the respiratory tissue. As more mutations of the initial SARS-CoV-2 wild-type with varying aggressiveness have been detected in the course of 2021, it became obvious that its genome is in a state of transformation and therefore a potential modification of the specific morphological appearance in CT may occur. The aim of this study was to quantitatively analyze the morphological differences of the SARS-CoV-2-B.1.1.7 mutation and wildtype variant in CT scans of the thorax.
We analyzed a dataset of 140 patients, which was divided into pneumonias caused by n=40 wildtype variants, n=40 B.1.1.7 variants, n=20 bacterial pneumonias, n=20 viral (non-COVID) pneumonias, and a test group of n=20 unremarkable CT examinations of the thorax. Semiautomated 3D segmentation of the lung tissue was performed for quantification of lung pathologies. The extent, ratio, and specific distribution of inflammatory affected lung tissue in each group were compared in a multivariate group analysis.
Lung segmentation revealed significant difference between the extent of ground glass opacities (GGO) or consolidation comparing SARS-CoV-2 wild-type and B.1.1.7 variant. Wildtype and B.1.1.7 variant showed both a symmetric distribution pattern of stage-dependent GGO and consolidation within matched COVID-19 stages. Viral non-COVID pneumonias had significantly fewer consolidations than the bacterial, but also than the COVID-19 B.1.1.7 variant groups.
CT based segmentation showed no significant difference between the morphological appearance of the COVID-19 wild-type variant and the SARS-CoV-2 B.1.1.7 mutation. However, our approach allowed a semiautomatic quantification of bacterial and viral lung pathologies. Quantitative CT image analyses, such as the one presented, appear to be an important component of pandemic preparedness considering an organism with ongoing genetic change, to describe a potential arising change in CT morphological appearance of possible new upcoming COVID-19 variants of concern.
由于新型冠状病毒肺炎具有独特的影像学特征，因此在胸部计算机断层扫描（CT）成像中以高特异性诊断2019冠状病毒病（COVID-19）感染被认为是可行的。由于其他病毒性非COVID肺炎大多表现出不同的分布模式，因此有理由认为，新型严重急性呼吸综合征冠状病毒2（SARS-CoV-2）引起的观察到的模式是其与呼吸组织相互作用时基因编码分子特性的结果。随着2021年期间检测到更多具有不同侵袭性的初始SARS-CoV-2野生型突变，很明显其基因组处于转变状态，因此CT中特定形态外观可能会发生潜在改变。本研究的目的是定量分析胸部CT扫描中SARS-CoV-2-B.1.1.7突变体和野生型变体的形态差异。
我们分析了140例患者的数据集，分为由40例野生型变体引起的肺炎、40例B.1.1.7变体引起的肺炎、20例细菌性肺炎、20例病毒性（非COVID）肺炎以及20例胸部CT检查无异常的测试组。对肺组织进行半自动三维分割以量化肺部病变。在多变量组分析中比较了每组中炎症累及肺组织的范围、比例和特定分布。
肺分割显示，比较SARS-CoV-2野生型和B.1.1.7变体时，磨玻璃影（GGO）或实变的范围存在显著差异。野生型和B.1.1.7变体在匹配的COVID-19阶段均显示出与阶段相关的GGO和实变的对称分布模式。病毒性非COVID肺炎的实变明显少于细菌性肺炎，但也少于COVID-19 B.1.1.7变体组。
基于CT的分割显示COVID-19野生型变体和SARS-CoV-2 B.1.1.7突变体的形态外观无显著差异。然而，我们的方法允许对细菌性和病毒性肺部病变进行半自动定量。考虑到一种基因不断变化的生物体，定量CT图像分析，如本文所展示的，似乎是大流行防范的重要组成部分，以描述可能出现的新的令人担忧的COVID-19变体在CT形态外观上可能出现的变化。
